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The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a…

统计方法学 · 统计学 2018-03-28 Michael Grayling , Adrian Mander , James Wason

For randomized controlled trials to be conclusive, it is important to set the target sample size accurately at the design stage. Comparing two normal populations, the sample size calculation requires specification of the variance other than…

统计方法学 · 统计学 2026-02-04 Hirotada Maeda , Satoshi Hattori , Tim Friede

Prior information is often incorporated informally when planning a clinical trial. Here, we present an approach on how to incorporate prior information, such as data from historical clinical trials, into the nuisance parameter based sample…

应用统计 · 统计学 2019-03-08 Tobias Mütze , Heinz Schmidli , Tim Friede

The 'gold standard' design for three-arm trials refers to trials with an active control and a placebo control in addition to the experimental treatment group. This trial design is recommended when being ethically justifiable and it allows…

应用统计 · 统计学 2017-03-22 Tobias Mütze , Frank Konietschke , Axel Munk , Tim Friede

The ability to accurately estimate the sample size required by a stepped-wedge (SW) cluster randomized trial (CRT) routinely depends upon the specification of several nuisance parameters. If these parameters are mis-specified, the trial…

统计方法学 · 统计学 2017-10-10 Michael Grayling , Adrian Mander , James Wason

This note investigates a number of scenarios in which unadjusted testing following a blinded sample size re-estimation leads to type I error violations. For superiority testing, this occurs in certain small-sample borderline cases. We…

统计方法学 · 统计学 2013-01-18 Ekkehard Glimm , Jürgen Läuter

Adapting the final sample size of a trial to the evidence accruing during the trial is a natural way to address planning uncertainty. Designs with adaptive sample size need to account for their optional stopping to guarantee strict type-I…

With the increasing availability of data from historical studies and real-world data sources, hybrid control designs that incorporate external data into the evaluation of current studies are being increasingly adopted. In these designs, it…

统计方法学 · 统计学 2025-06-23 Masahiro Kojima , Shunichiro Orihara , Keisuke Hanada , Tomohiro Ohigashi

Trials enroll a large number of subjects in order to attain power, making them expensive and time-consuming. Sample size calculations are often performed with the assumption of an unadjusted analysis, even if the trial analysis plan…

统计方法学 · 统计学 2021-07-06 Alejandro Schuler

Binary endpoints are common in clinical trials and conditional odds ratios have traditionally been used to assess treatment effects. However, the interpretation of odds ratios is difficult, they are non-collapsible and rely on strong…

统计方法学 · 统计学 2026-05-20 Martin Schnuerch , Alex Ocampo , Klaus Kähler Holst , Christian Stock

Time-to-event endpoints show an increasing popularity in phase II cancer trials. The standard statistical tool for such one-armed survival trials is the one-sample log-rank test. Its distributional properties are commonly derived in the…

统计方法学 · 统计学 2026-03-02 Moritz Fabian Danzer , Andreas Faldum , Rene Schmidt

G-computation has become a widely used robust method for estimating unconditional (marginal) treatment effects with covariate adjustment in the analysis of randomized clinical trials. Statistical inference in this context typically relies…

统计方法学 · 统计学 2025-03-18 Xin Zhang , Haitao Chu , Lin Liu , Satrajit Roychoudhury

This paper investigates type I error violations that occur when blinded sample size reviews are applied in equivalence testing. We give a derivation which explains why such violations are more pronounced in equivalence testing than in the…

应用统计 · 统计学 2021-09-08 Ekkehard Glimm , Lillian Yau , Heike Woehling

Randomized controlled clinical trials provide the gold standard for evidence generation in relation to the efficacy of a new treatment in medical research. Relevant information from previous studies may be desirable to incorporate in the…

统计方法学 · 统计学 2024-05-29 Lou E. Whitehead , James M. S. Wason , Oliver Sailer , Haiyan Zheng

When observations are independent, formulae and software are readily available to plan and design studies of appropriate size and power to detect important associations. When observations are correlated or clustered, results obtained from…

应用统计 · 统计学 2021-03-15 David M. Thompson

Simulation offers a simple and flexible way to estimate the power of a clinical trial when analytic formulae are not available. The computational burden of using simulation has, however, restricted its application to only the simplest of…

统计方法学 · 统计学 2020-12-04 Duncan T. Wilson , Rebecca E. A. Walwyn , Richard Hooper , Julia Brown , Amanda J. Farrin

External controls (ECs) from historical trials or real-world data have gained increasing attention as a way to augment hybrid and single-arm trials, especially when balanced randomization is infeasible. While most existing work has focused…

统计方法学 · 统计学 2025-12-15 Yujing Gao , Xiang Zhang , Shu Yang

Restricted mean survival time (RMST) is gaining attention as a measure to quantify the treatment effect on survival outcomes in randomized clinical trials. Several methods to determine sample size based on the RMST-based tests have been…

统计方法学 · 统计学 2022-12-19 Satoshi Hattori , Hajime Uno

The use of historical controls offers a valuable alternative when traditional randomized controlled trials are not feasible. However, such approaches may introduce bias due to temporal changes in patient populations, diagnostic criteria,…

统计方法学 · 统计学 2025-12-24 Marco Ratta , Pavel Mozgunov , Sandrine Boulet , Moreno Ursino

Clinical trials with a hybrid control arm (a control arm constructed from a combination of randomized patients and real-world data on patients receiving usual care in standard clinical practice) have the potential to decrease the cost of…

统计方法学 · 统计学 2021-08-20 Joanna Harton , Brian Segal , Ronac Mamtani , Nandita Mitra , Rebecca Hubbard
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